Satellite computing, as an emerging edge computing paradigm, extends computing and networking services into space. Due to the internal design constraints of low-Earth orbit (LEO) satellites and the challenges posed by the external environment, satellite computing faces inherent limitations, including severely constrained resources, non-rechargeable batteries, poor heat dissipation, and highly dynamic operating conditions, leading to unreliable and unsustainable quality of service. To address the above challenges and fully realize the potential of satellite computing, this paper investigates temperature- and energy-aware dynamic task scheduling and computing resource allocation, aiming to optimize service latency, reduce onboard energy consumption, and enhance operational profit. Solving this problem requires coordinating task scheduling and resource allocation, balancing communication and computation latency, and addressing the challenge of a vast search space. To solve the above challenges, we first formulate this problem as a repeated Stackelberg game by developing temperature and energy models. Through theoretical analysis, we show that this game leads to a convex optimization framework that exhibits exponential complexity. To accelerate the search for the Stackelberg equilibrium solution, we propose a dynamic task scheduling algorithm based on the interior point method, which reduces the computational complexity to polynomial order. Trace-driven simulations demonstrate that the proposed algorithm reduces task scheduling latency by 28.4% and improves utility by 13% on average.

Internet of Things (IoT) devices are being used widely in the fields of smart city, smart grid, environmental monitoring, Internet of Vehicles and other fields that need large-scale sensing data. However, the research on storage and computation power for IoT is still in its early stages. The game theory converts the interaction between two IoT devices into a game where the conflict is resolved by utilizing the game’s equilibrium conditions. Our goal with the game theory is to maximize the utility for every device in the IoT network. In this article, we review the recent game-theory-based solutions proposed in IoT networks. We summarize game theory concepts and categorize the common game models for ease of understanding for the reader. Later, we focus on analyzing solutions proposed in resource allocation, task scheduling, node selection, quality of service, and network security. Finally, we summarize research challenges and propose future research directions.

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real‐time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long‐term participation of users and high quality of tasks. This paper designs a long‐term incentive mechanism based on game theory. The long‐term incentive mechanism is to encourage participants to provide long‐term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game‐based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long‐term participation and high‐quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long‐term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright‐Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long‐term incentive effect for participants in mobile crowdsourcing systems.